Method and network entity for evaluating a link between a first network node and a second network node

ABSTRACT

A method and a network entity for evaluating a link between a first and a second network node are disclosed. The link is configured to carry data packets between the network nodes via a third network node. The link comprises a first segment and a second segment. The network entity obtains an indication of a measurement tool to be used in a measurement session for evaluation of the link. The network entity selects a mathematical model based on the indication. The network entity generates measurement values by executing the measurement session. The network entity determines a first and a second value relating to lost data packets of the first and second segments, respectively, based on the measurement values and the selected mathematical model. The network entity identifies at least one of the first and second segments based the first and second values.

TECHNICAL FIELD

Embodiments herein relate to communication networks, such astelecommunication networks and computer networks. Furthermore,embodiments herein illustrate a method and a network entity forevaluating a link between a first and a second network node.

BACKGROUND

Institute of Electrical and Electronics Engineers (IEEE) and InternetEngineering Task Force (IETF) has defined network node capabilities forperformance and fault management through the means of Operations,Administration and Management (OAM) protocols. In general, suchprotocols are handled by two types of entities located on the networknode; Maintenance End-Points (MEP) and Maintenance Intermediate Points(MIP). The MEPs are located at the Ingress and egress of a network path,a tunnel, a virtual circuit, a link or a service. The MIPs are situatedat the nodes that are part of the network path between the ingress andegress of the network path. Currently, the MEPs are active, which meansthat they can initiate measurement sessions. In contrast, the MIPs arepassive, which means that they may only reply to requests or forwardmeasurement packets that are not addressed to them.

The OAM tools for fault management and performance analysis are based onthe standardization work of IETF and the International TelecommunicationUnion (ITU). For example, recommendations for Ethernet OAM functions aregiven in ITU-T Rec. Y.1731, titled “OAM Functions and Mechanisms forEthernet Based Networks”, issue 02/2008. For Multi-Protocol LabelSwitching Transport Profiles (MPLS-TP), performance measurements aredefined in RFC 6374, titled “Packet Loss and Delay Measurement for MPLSNetworks” to D. Frost and S. Bryant, dated September 2011. For InternetProtocol (IP) networks, a so called trace-route tool is specified in RFC1393, titled “Trace-route Using an IP Option” to G. Malkin, NetworkWorking Group, from 1993.

An overview of existing OAM protocols for fault management andperformance monitoring at different layers, e.g. MPLS, IP, and Ethemet,is presented in IEEE Communications Magazine, vol. 43, pp. 152-157, Nov.2005, “Ethernet OAM: Key Enabler for Carrier class Metro EthernetServices” to M. McFarland, S. Salam, and R. Checker. In this overview,necessary usage requirements in the continued standardization work ofthe protocols are discussed.

With existing OAM tools localization of performance degradations onsegments between MIPs is cumbersome due to additional measurements. Inmany cases, it is even impossible to achieve such localization ofperformance degradations or anomalies on links between MIPs.

FIG. 1 shows a known network, comprising MEPs and MIPs. The MIPS areshown as triangles. An expected success rate E for links Y₁ to Y₄ is tobe determined by use of a trace-route tool. A link, see Y₄, comprisessegments X₁ to X₄.

Table 1 shows an exemplifying result from a trace-route tool when run inthe network of FIG. 1. The first column of table 1 presents the numberof the segment, i.e. the number of each of X₁ to X₄. The following threecolumns comprise the delay measured between the MEP and a particularMIP, e.g. the links Y₁ to Y₄. The numbering of the segments coincideswith numbering of MIPs. Sometimes a MIP is referred to as a hop. Though,the last hop is the egress MEP for the trace-route tool. A segment maysometimes be referred to as a hop. Letters A through J representIP-addresses, such as 66.249.95.219, www.mydomain.com, for differentMIPs.

TABLE 1 Output from a known trace-route tool Hop # T1 (ms) T2 (ms) T3(ms) MIP 1 19 1 1 A 2 1 1 1 B 3 2 2 2 C 4 24 40 22 D 5 3 2 2 E 6 22 2223 F 7 2 7 2 G 8 3 3 2 H 9 22 22 22 I 10 21 21 21 J

For hop 4 in the left-most column, it can be observed that some of theprobe packets exhibit a significantly higher delay than other probepackets, i.e. 40 ms, compared to 22-24 ms. A similar artifact isexhibited on line 7, where we have 7 ms compared to 2 ms. If a networkoperator tries to determine which segment is responsible for theincrease in delay, the information made available by the trace-routetool is not enough in order to do an unambiguous identification of thesegment, or segments, where the degradation occurred.

In the example above, an operator may conclude that the segments betweenMIPs 3-4 and 6-7 are degraded. However, such a judgment actuallydisregards the fact that the trace-route probe packets are sentindividually at pre-defined intervals and thus encounter differentnetwork conditions. Hence, a degraded segment, e.g. in the form of atransient problem, observed due to report of delayed packet to MIP 4 mayhave finished by the time another packet, which was sent to MIP 7,arrives.

Therefore, a problem in relation of the example above is that manualprocesses for identifying segments responsible for performancedegradations in packet networks are error-prone and provide ambiguousresults.

Furthermore, it is known to operate OAM tools proactively. This meansthat the OAM tool is run periodically, with a fixed time interval thatis configured when the OAM tool is installed into a network. The fixedtime interval is kept constant as long as the proactive operations modeis enabled. With a short fixed time interval, a high rate of invocationsof the tool is obtained. The network is then loaded with traffic,generated by the OAM tool. Hence, performance, in terms of capacity forother traffic than that generated by the OAM tool, is degraded.Oppositely, a low rate of invocations of the OAM tool is likely to missdegradations that were short-lived, but still may affect performance ofthe network negatively.

According to known solutions, the above mentioned problem may be solvedby the provision of dedicated probing nodes. This means that the MIPsand MEPs are replaced by the dedicated probing nodes, each of whichincludes dedicated management software.

In case an OAM tool, based on the above mentioned Y.1731 for EthernetOAM functions, is employed, the dedicated probing nodes cannot be used,because the MEPs cannot be placed on the data plane other than on theingress and egress of a particular tunnel, such as an E-LINE. Moreover,the use of dedicated probing nodes is rather expensive to install andoperate. In addition, planning with respect to the placement of theprobing nodes within the network is required. Attempts in applyingmethods resembling dedicated probing have been done as in e.g.http://www.jdsu.com/ProductLiterature/EthemetAccess_TN_CPO_TM_AE.pdf.Disadvantageously, flexibility of the network is also reduced, since theplacement of the probing nodes needs to be re-planned in response to anychanges in the topology of the network.

SUMMARY

An object is provide an improved method for operations, administrationand management of a network of the above mentioned kinds, which methodovercomes or at least alleviates the above mentioned problems and/ordisadvantages.

According to an aspect, the object is achieved by a method forevaluating a link between a first network node and a second networknode. The link is configured to carry data packets between the first andsecond network nodes via at least one third network node. The linkcomprises at least a first segment configured to carry data packetsbetween the first and third network nodes and a second segmentconfigured to carry data packets between the second and third networknodes. The network entity obtains an indication of a measurement tool tobe used in a measurement session for evaluation of the link. The networkentity selects a mathematical model based on the indication of themeasurement tool. The network entity generates a set of measurementvalues by executing the measurement session while using the measurementtool according to the indication of the measurement tool. The networkentity determines a first and a second value relating to lost datapackets of the first and second segments, respectively, based on the setof measurement values and the selected mathematical model. The networkentity identifies at least one of the first and second segments basedthe first and second values.

According to another aspect, the object is achieved by a network entityconfigured to evaluate a link between a first network node and a secondnetwork node. The link is configured to carry data packets between thefirst and second network nodes via at least one third network node. Thelink comprises at least a first segment configured to carry data packetsbetween the first and third network nodes and a second segmentconfigured to carry data packets between the second and third networknodes. The network entity comprises a processing circuit configured toobtain an indication of a measurement tool to be used in a measurementsession for evaluation of the link. The processing circuit is furtherconfigured to select a mathematical model based on the indication of themeasurement tool and to generate a set of measurement values byexecuting the measurement session while using the measurement toolaccording to the indication of the measurement tool. Moreover, theprocessing circuit is configured to determine a first and a second valuerelating to lost data packets of the first and second segments,respectively, based on the set of measurement values and the selectedmathematical model. Furthermore, the processing circuit is configured toidentify at least one of the first and second segments based the firstand second values.

Embodiments herein enable inferring, or determining, per-segment delayand loss estimates based on results collected from OAM and measurementtools between the first network node and the at least one third networknode along the link in a packet network. In this manner, degradations orchanges of the link may be localized to the first and/or second segment.Such localization of degradations is a first step towards takingcorrective actions to improve performance, in for example terms of droprate, of the network.

In contrast to network monitoring methods that require dedicated probingnodes as described above, the embodiments herein offers reliable andresource-efficient means for network monitoring and performanceanalysis, while requiring a constant, relatively small, amount of memorythat mainly scales with the number of statistical counters per observedsegment, such as the first and second segments. According to embodimentherein few arithmetic operations are required. Furthermore, a set ofcounter that scales with the number of segments are used. Embodimentsherein may therefore be implemented to operate in network environmentsin which the computational resources are very restricted.

BRIEF DESCRIPTION OF THE DRAWINGS

The various aspects of embodiments disclosed herein, includingparticular features and advantages thereof, will be readily understoodfrom the following detailed description and the accompanying drawings,in which:

FIG. 1 shows a schematic overview of a known network,

FIG. 2 shows a schematic overview of an exemplifying network, in whichembodiments herein may be implemented,

FIG. 3 shows a schematic flowchart illustrating methods according toembodiments herein,

FIG. 4 is a schematic block diagram illustrating an exemplifying networkentity configured to perform the methods illustrated in FIG. 3.

FIGS. 5 a and 5 b show schematic flowcharts illustrating methodsaccording to embodiments herein,

FIG. 6 is a schematic block diagram illustrating exemplifying networknodes according to embodiments herein,

FIGS. 7 a and 7 b show schematic flowcharts illustrating methodsaccording to embodiments herein, and

FIG. 8 is a schematic block diagram illustrating exemplifying networknodes according to embodiments herein.

DETAILED DESCRIPTION

Throughout the following description similar reference numerals havebeen used to denote similar elements, network nodes, parts, items orfeatures, when applicable. In the Figures, features that appear in someembodiments are indicated by dashed lines unless otherwise indicated inthe text.

Before the embodiments are described in more detail, some observationsregarding probes will be discussed. A probe may sometimes be referred toas a measurement message, such as trace-route or loopback message.

Probes sent between MEPs are handled differently depending on whetherthe probes can be regarded as one single measurement or not. In view ofstatistical modelling, this distinction between when a probe can beregarded as a single measurement or not, it controls how themeasurements are modeled. This means that probes are modelled asdependent or as independent measurements. The dependent measurements maybe successive hop-by-hop measurements, e.g. accounting for co-variationbetween the hops. The independent measurements originate from the MEPwith successive hop increments.

For dependent measurements, it is required that the probes can behandled as one single observation of the link, capturing the momentousnetwork behavior. This requirement is fulfilled when the probingapproach is based on successive hop-by-hop increments, such that eachmeasured segment depends on the measured outcome of previous segments.In case a burst of probes measures the connection, it is necessary thatthe connection is statistically stationary such that the dependencyrelation between the probes can be preserved.

For independent measurements, a different model is used such that eachprobe is handled without accounting for any covariance between the hops.The independence assumption implies a statistically non-stationarycondition on the connection. This means that the connection variesbetween each sent probe in a burst, and that each probe is regarded asone independent observation of the connection.

Which model for modelling of the measurements depends on whethersuccessive probing or probe bursts will be used, and whether the link isstatistically stationary or non-stationary.

Probes π_(t) are periodically sent from the originating MEP andincremented for measuring latency and/or drop over a link Y_(i)consisting of segments X₁, X₂, . . . , X_(i) towards the MIP/MEP node atn_(i)εN. This is shown in FIG. 1, in which the probes 101 to 104 aresent from MEP A_(i).

The outcome of each measurement is stored in a set of statisticalcounters for each segment. Once the measurements over the link Y_(i) arecollected, necessary statistics such as mean and variance on individualsegments X_(i) can be extracted mathematically from statistical countersfor observed link latency and drop.

Stationary and non-stationary measurement conditions are likely toinfluence how link loss is modelled relative the probing strategy.

However, if the available probing tools allows it, individual link losscan also be modelled independently from stationary or non-stationarymeasurement conditions, and is here based on the assumption that linkdrop, or rather the success rate, on each link is stochasticallyindependent, such that E(X₁X₂)=E(X₁)E(X₂). Previous studies indicatethat temporal correlation in link drop depends mainly on the time scalewithin which drop is measured. See for example “J. C. Bolot. End-to-EndPacket Delay and Loss Behavior in the Internet. Proc. SIGCOMM '93, pp.289-298, Sept. 1993”, “Y. Yang, M. Kim, and S. Lam. Transient Behaviorsof TCP-friendly Congestion Control Protocols. In Proceedings of theConference on Computer Communications (IEEE Infocom) (Anchorage, Ak.,April 2001), pp. 1716-1725”, “M. Yajnik, S. Moon, J. Kurose, and D.Towsley. Measurement and modeling of the temporal dependence in packetloss. In Proceedings of IEEE INFOCOM '99, March 1999”. This implies thatlink drop can be sufficiently modelled under the assumption ofstatistical independence, provided that: 1) the probes do not havesignificant impact on the link load, and 2) that the connection is freefrom congestion.

In the stochastically independent setting, each measurement may be aburst of probes, or may be one probe that is forwarded hop-by-hop untila drop occurs and that successively sends probe replies backwards to theoriginating MEP. Regardless of the measurement approach, the 0/1 outcomeof a packet being transmitted over the link is

$Y_{i} = {\prod\limits_{j = 1}^{i}\; {X_{j}.}}$

The success rate of a link Y, is under the independence assumption

${E\left( Y_{i} \right)} = {\prod\limits_{j = 1}^{i}\; {{E\left( X_{j} \right)}.}}$

The expected success rate on the individual link X_(i) can therefore becomputed from:

$\begin{matrix}{{E\left( X_{i} \right)} = \frac{E\left( Y_{i} \right)}{E\left( Y_{i - 1} \right)}} & {{equation}\mspace{14mu} 1}\end{matrix}$

or alternatively:

$\begin{matrix}{{E\left( X_{i} \right)} = \frac{{E\left( {Y_{t}Y_{i - 1}} \right)} - {{Cov}\left( {Y_{i},Y_{i - 1}} \right)}}{{E\left( Y_{i - 1} \right)}^{2}}} & {{equation}\mspace{14mu} 2}\end{matrix}$

and from equation 1 or 2 the drop rate for segment X_(i) can betrivially computed from 1−E(X_(i)).

Note that, as the drop rate clearly can be modelled based on a discreteBernoulli distribution with outcome 0/1, estimations of the variance istrivial once the mean success rate is obtained.

It is necessary that the observed successful transactions between theoriginating MEP and the endpoint always are E(Y_(i-1))≧E(Y_(i)).Whenever this condition is not fulfilled, it is necessary to setE(Y_(i-1))=E(Y_(i)) in order to keep 0≦E(X_(i))≦1 such that the successrate would on certain segments be 1, thereby representing a perfectsegment in terms of no drop. In practice, this situation can occur as aneffect of random fluctuations in the sampling process. As suchconditions reflect uncertainty it is necessary to perform additionalprobes to obtain reasonable statistics.

The obtained statistics can then be used for parameter estimation ifdesired. As statistical independence is assumed, a Bernoullidistribution is here sufficient to model link drop, representing the 0/1outcome of a probe on each link. In R. Gaeta, M. Gribaudo, D. Manini,and M. Sereno. On the use of Petri nets for the computation ofcompletion time distribution for short TCP transfers. Applications andTheory of Petri Nets, 24th International Conference, pages 181-200,Springer, 2003, modelling link drop as a Bernoulli distribution isdiscussed. The success rate parameter of the distribution can beestimated from the maximum likelihood of the observations, which relatesdirectly to the observed average success rate, λ:

λ_(i) =E(X _(i))  equation 3

For long-term adaptation and comparison of estimates (e.g. for thepurpose of change detection), the estimate in equation 3 can be furthermodified to take prior estimates into account:

$\begin{matrix}{\lambda_{i} = \frac{{{nE}\left( X_{i} \right)} + {\alpha \; \lambda_{i}^{*}}}{n + \alpha}} & {{equation}\mspace{14mu} 4}\end{matrix}$

where α controls the impact of the prior in the new estimate, and n isthe number of observed samples.

FIG. 2 depicts an exemplifying communication network 100 in whichembodiments herein may be implemented. In this example, thecommunication network is a MPLS-TP network. In other examples, thecommunication system may be an IP network, an Ethemet network or thelike.

The communication network 100 comprises a first network node 110, suchas a first MEP, and a second network node 120, such as a second MEP.

Furthermore, the communication network 100 comprises a third networknode 130, such as a MIP. It shall be understood that only one thirdnetwork node is shown for simplicity. That is to say, in other examples,the communication network 100 may comprise further third network nodes.

According to some examples, the first network node 110 may be comprisedin a network entity 140.

The network entity 140 may comprise a network management node 150, suchas a network management system (NMS).

A link between the first and second network nodes 110, 120 comprises afirst segment 161 and a second segment 162. The first segment connectsthe first network node 110 to the third network node 130. The secondsegment connects the second network node 120 to the third network node130.

FIG. 3 illustrates an exemplifying method for evaluating the linkbetween the first and second network nodes 110, 120. The link isconfigured to carry data packets between the first and second networknodes 110, 120 via at least one third network node 130. The linkcomprises at least a first segment configured to carry data packetsbetween the first and third network nodes 110, 130 and a second segmentconfigured to carry data packets between the second and third networknodes 120, 130. The link may be a multi-segment Ethemet link,multi-segment Internet Protocol link, a multi-segment pseudo-wire or aMPLS-TP label switched path or the like. The link does to change duringthe measurement session, i.e. the underlying topology is fixed during ameasurement session as in action 306.

In this example, the method is performed by the network entity 140. Asmentioned above, the network entity 140 may be the first network node110 or the network entity 140 may be the network management node 150.

The following actions may be performed in any suitable order.

Action 301

The network entity 140 obtains an indication of a measurement tool to beused in a measurement session for evaluation of the link.

When the network entity 140 is the first network node 110, the networkentity 140 obtains the indication of the measurement tool by receivingthe indication of the measurement tool from a network management node150.

When the network entity 140 is the network management node 150, thenetwork entity 140 obtains the indication of the measurement tool byreceiving the indication of the measurement tool from an operator.

This means that the indication of the measurement tool may be manuallyselected by the operator. For example, the operator may specify whichtools should be used, e.g. use this method only for MPLS-TP LM tools,and not for Ethernet Y.1731 tools that are deployed in the same network.

Furthermore, the operator may choose to run an investigation manuallyon-demand for a particular measurement tool, and then the networkmanagement system would assist in choosing the correct models andintermediary nodes, plus automatically performing the measurements anddetermining the segment that is degraded, or problematic.

Action 304

The network entity 140 may identify a set of mathematical models adaptedto the measurement session while taking into account whether datapackets carried on the link are modelled under a statistical conditionof stationarity or non-stationarity. Stationary or non-stationaryconditions for the link has been explained above. In action 305, amathematical model is selected from the set of mathematical models.

Action 305

The network entity 140 selects a mathematical model based on theindication of the measurement tool.

The selected mathematical model may be equation 1 or 2. As mentionedabove, equation 1 is

${E\left( X_{i} \right)} = \frac{E\left( Y_{i} \right)}{E\left( Y_{i - 1} \right)}$

and equation 2 is

${{E\left( X_{i} \right)} = \frac{{E\left( {Y_{i}Y_{i - 1}} \right)} - {{Cov}\left( {Y_{i},Y_{i - 1}} \right)}}{{E\left( Y_{i - 1} \right)}^{2}}},$

where X_(i) is a segment, E(X_(i)) is the estimated success rate on thesegment X_(i), E(Y_(i)) is the observed success rate for link Y_(i)(i.e. the average of successfully transmitted packets from the MEP overall segments X included in Y), Cov is covariance.

Action 306

The network entity 140 generates a set of measurement values byexecuting the measurement session while using the measurement toolaccording to the indication of the measurement tool. In this manner, thenetwork entity 140 collects the set of measurement values relating tothe link to be evaluated.

Action 307

The network entity 140 determines a first and a second value relating tolost data packets of the first and second segments, respectively, basedon the set of measurement values and the selected mathematical model.

Action 308

The network entity 140 identifies at least one of the first and secondsegments based the first and second values. Said at least one of thefirst and second segments may be identified as changed, for exampledegraded or available if previously not available. The change may beexpressed in terms of lost data packets. In this context, “available”may be that the first and/or second segment can, or is able to, carrydata at a certain bit rate.

In some examples, the network entity 140 identifies at least one of thefirst and second segments by selecting a predefined number of the firstand second segments for which the respective first and second values arethe greatest among the first and second values. As an example, when thepredefined number is two, the network entity 140 may select two segmentsthat are associated to the two greatest values among the at least thefirst and second values.

In some examples, the network entity 140 identifies at least one of thefirst and second segments by selecting one of more of the first andsecond segments for which the respective first and second values aregreater than a first predetermined threshold value for lost datapackets. In this example, the network entity 140 selects those segmentsfor which the respective value is higher than the first predeterminedthreshold.

In some examples, the network entity 140 identifies at least one of thefirst and second segments by determining a respective value indicativeof a change in terms of lost data packets for each of the first andsecond segments based on the first and second value, respectively, andby selecting one or more of the first and second segments for which therespective value indicative of the change is greater than a secondpredetermined threshold value for change detection.

With reference to FIG. 4, a schematic block diagram of the networkentity 140 is shown. The network entity 140 is configured to evaluate alink between a first network node 110 and a second network node 120.

As mentioned, the link is configured to carry data packets between thefirst and second network nodes 110, 120 via at least one third networknode 130. The link comprises at least a first segment configured tocarry data packets between the first and third network nodes 110, 130and a second segment configured to carry data packets between the secondand third network nodes 120, 130. The link may be a multi-segmentEthernet link, multi-segment Internet Protocol link, a multi-segmentpseudo-wire or a MPLS-TP label switched path or the like.

Again, the network entity 140 may be the first network node 110 or thenetwork management node 150.

The network entity 140 comprises a processing circuit 410 configured toobtain an indication of a measurement tool to be used in a measurementsession for evaluation of the link. The processing circuit 410 mayfurther be configured to receive the indication of the measurement toolfrom a network management node 150. The processing circuit 410 mayfurther be configured to receive the indication of the measurement toolfrom an operator.

The processing circuit 410 is further configured to select amathematical model based on the indication of the measurement tool.

The processing circuit 410 may further be configured to identify a setof mathematical models adapted to the measurement session while takinginto account whether data packets carried on the link are modelled undera statistical condition of stationarity or non-stationarity, wherein theselected mathematical model is selected from the set of mathematicalmodels.

The selected mathematical model may be equation 1 or 2. As mentioned,equation 1 is

${E\left( X_{i} \right)} = \frac{E\left( Y_{i} \right)}{E\left( Y_{i - 1} \right)}$

and equation 2 is

${{E\left( X_{i} \right)} = \frac{{E\left( {Y_{i}Y_{i - 1}} \right)} - {{Cov}\left( {Y_{i},Y_{i - 1}} \right)}}{{E\left( Y_{i - 1} \right)}^{2}}},$

where X_(i) is a segment, E(X_(i)) is the estimated success rate on thesegment X_(i), E(Y_(i)) is the observed success rate for link Y_(i)(i.e. the average of successfully transmitted packets from the MEP overall segments X included in Y), Cov is covariance.

The processing circuit 410 is further configured to generate a set ofmeasurement values by executing the measurement session while using themeasurement tool according to the indication of the measurement tool;

The processing circuit 410 is further configured to determine a firstand a second value relating to lost data packets of the first and secondsegments, respectively, based on the set of measurement values and theselected mathematical model.

The processing circuit 410 is further configured to identify at leastone of the first and second segments as degraded, in terms of lost datapackets, based the first and second values.

The processing circuit 410 may further be configured to select apredefined number of the first and second segments for which therespective first and second values are the greatest among the first andsecond values.

The processing circuit 410 may further be configured to select one ofmore of the first and second segments for which the respective first andsecond values are greater than a first predetermined threshold value forlost data packets.

The processing circuit 410 may further be configured to determine arespective value indicative of a change in terms of lost data packetsfor each of the first and second segments based on the first and secondvalue, respectively, and to select one or more of the first and secondsegments for which the respective value indicative of the change isgreater than a second predetermined threshold value for changedetection.

The processing circuit 410 may be a processing unit, a processor, anapplication specific integrated circuit (ASIC), a field-programmablegate array (FPGA) or the like. As an example, a processor, an ASIC, anFPGA or the like may comprise one or more processor kernels.

The network entity 140 further comprises a transmitter 420, which may beconfigured to send one or more values and/or parameters describedherein.

The network entity 140 further comprises a receiver 430, which may beconfigured to receive one or more values and/or parameters describedherein.

The network entity 140 further comprises a memory 440 for storingsoftware to be executed by, for example, the processing circuit. Thesoftware may comprise instructions to enable the processing circuit toperform the method in the network entity 140 as described above inconjunction with for example FIG. 3. The memory may be a hard disk, amagnetic storage medium, a portable computer diskette or disc, flashmemory, random access memory (RAM) or the like. Furthermore, the memorymay be an internal register memory of a processor.

With reference to FIGS. 5 a and 5 b, a further embodiment is described.In this example, the network entity 140 is an ingress MEP, which is anexample of the first network node 110.

In this scenario, the network management node 150, such as a networkmanagement system (NMS), performs one or more of the following actions.

Action 501

The network management node 150 determines the ingress and egress MEPs.

Action 502

The network management node 150 determines a set of MIPs to be probed.

Action 603

The network management node 150 selects a measurement type (loss) andtool based on probing strategy. Probing strategy is one of singleunicast packet, burst of unicast packets, or multicast packet on apoint-to-point service, and is determined based on a table where eachtool is associated to a probing strategy.

Action 504

The network management node 150 may characterize network conditions andprepare information for the MEP. The information may be, for example,which models are appropriate be used for this measurement session. Asimple array of (model name, true/false) may be used to describe this.

Action 505

The network management node 150 configures the ingress and egress MEPsand the set of MIPs to be probed.

In co-operation with the network management node 150, the first networknode 110 performs one or more of the following actions. Reference is nowmade to FIG. 5 b.

Action 506

The first network node 110 determines statistical model, i.e.stationary, non-stationary, etc, based on measurement type, probingstrategy and, optionally, information from the network management node150.

Action 507

The first network node 110 sends probe(s) from ingress MEP to set ofMIPs. Furthermore, the first network node 110 starts a timer forindicating time to receiver, a time_to_receive timer. In some examples,as a technical note, the time_to_receive is equal to delay_until.

Action 508

The first network node 110 waits to receive results, e.g. reports fromthe probes sent in action 507. When the first network node 110 hasreceived reports from all probes the timer time_to_receive is stopped.

Action 509

The first network node 110 updates values while using the results.

For loss:

During stationary conditions: update values based on equation 1 or 2.

During non-stationary conditions: update values based on equation 1 or2.

Action 510

The first network node 110 determines, or identifies, one or moresegment with an updated value above a given threshold, or top N segmentswith losses above a given threshold.

Action 511

The first network node 110 may run change detection based on equation 4and determine segments where changes are significant.

Action 512

The first network node 110 sends information about one or more segmentsto a user-interface device, such as a display or the like. In thismanner, a human operator may be informed about degraded segments, e.g.by triggering an alarm sound, a visual alarm, a tactile alarm or thelike.

Action 513

The first network node 110 may calculate a delay, delay_until_next,until sending the next probe by use of e.g. equation 5 and 6 below.

Action 514

The first network node 110 waits a period given by(delay_until_next-time_to_receive).

Actions 507 through action 514 are repeated until the measurementsession is ended by the network management node 150.

FIGS. 6 and 8 show exemplifying ingress MEPs and exemplifying NMSs. Inthis way, briefly explain differences between the embodiments of FIG. 5and FIG. 7 below may be illustrated. First, continuing with embodimentsrelating to FIG. 5 and then proceeding with embodiments relating to FIG.7.

FIG. 6 shows an exemplifying ingress MEP and an exemplifying NMS, whichare configured to perform the methods illustrated in FIGS. 5 a and 5 b.

The ingress MEP includes one or more electric circuits and/or softwaremodules to handle measurement analysis models, adaptive intervalcalculation, communication with the NMS and the like.

Furthermore, the ingress MEP includes a set of measurement tools for OAMpurposes.

The NMS includes information about MEPs and MIPs, measurement type.Moreover, the NMS includes electric circuits and/or software modules forcalculating the time interval.

According to a further embodiment, as illustrated in FIG. 7 a, thenetwork entity 140 is a network management system, which is an exampleof the network management node 150.

The network management node 150 may perform one or more of the followingactions.

Action 701

The network management node 150 determines the ingress and egress MEPs.This action is the same as action 501.

Action 702

The network management node 150 determines a set of MIPs to be probed.This action is the same as action 502.

Action 703

The network management node 150 selects a measurement type (loss) andtool based on probing strategy. Probing strategy is one of singleunicast packet, burst of unicast packets, or multicast packet on apoint-to-point service and is determined based on a table where eachtool is associated to a probing strategy. This action is the same asaction 503.

Action 704

The network management node 150 may characterize network conditions andprepare information for the MEP. The information may be, for example,which models are appropriate be used for this measurement session. Asimple array of (model name, true/false) may be used to describe this.This action is the same as action 504.

Action 705

The network management node 150 configures the ingress and egress MEPsand the set of MIPs to be probed. This action is the same as action 505.

Action 706

The network management node 150 determines probing strategy based onexisting tools for the measurements to be performed.

Action 707

The network management node 150 determines statistical model, such asstationary, non-stationary, etc, based on measurement type and probingstrategy. This action is similar to action 506.

Action 708

The network management node 150 sends initial delay between pro-activemeasurements.

Action 709

The network management node 150 waits to receive results for the set ofMIPs.

Action 710

The network management node 150 updates values.

For loss:

-   -   During stationary conditions: update values based on equation 1        or 2.    -   During non-stationary conditions: update values based on        equation 1 or 2.

Action 711

The network management node 150 determines segments with updated valuesabove a given threshold, or top N segments with losses above a giventhreshold.

Action 712

The network management node 150 may execute change detection based onequation 4 and determine link segments where changes are significant.

Action 713

The network management node 150 sends information about one or moresegments to a user-interface device, such as a display or the like. Inthis manner, a human operator may be informed about degraded segments,e.g. by triggering an alarm sound, a visual alarm, a tactile alarm orthe like.

Action 714

The network management node 150 may calculate delay until next probeusing for example equation 5 and 6.

Action 715

The network management node 150 may send the delay calculated in action714. The delay indicates time until next probe to ingress MEP.

Alternatively, the network management node 150 sends a new delay only ifdifference higher then threshold.Actions 709 to 715 are repeated for each ingress MEP when multiple linksare evaluated. For each evaluated link, there may be a respective model.

In co-operation with the network management node 150, the first networknode 110 performs one or more of the following actions. Reference is nowmade to FIG. 7 b.

Action 720

The first network node 110 receives an initial delay from the networkmanagement node 150. See action 708.

Action 721

The first network node 110 receives information about the set of MIPsfrom the network management node 150. The information about the set ofMIPs may be sent by the network management node 150 in action 708.

Action 722

The first network node 110 waits a time interval given by the delayreceived in action 720 or 725.

Action 723

The first network node 110 sends probe(s) to the set of MIPs.

Action 724

The first network node 110 may report results from the probe(s) to thenetwork management node 150.

Action 725

The first network node 110 may update the delay based information fromthe network management node 150. For example, the first network node 110may check if it received updated delay information and if so the firstnetwork node 110 updates the delay. The updated delay is then used inaction 722.

Actions 722 to 725 are repeated until the measurement session is endedby the network management node 150.

FIG. 8 shows an exemplifying ingress MEP and an exemplifying NMS, whichare configured to perform the methods illustrated in FIGS. 7 a and 7 b.

In contrast to FIG. 6, the ingress MEP now includes one or more electriccircuits and/or software modules to execute a set of measurement toolsfor OAM purposes. The one or more electric circuits and/or softwaremodules relating to measurement analysis models, adaptive intervalcalculation have been removed.

The NMS includes information about MEPs and MIPs, measurement type.Moreover, the NMS includes electric circuits and/or software modules forcalculating the time interval.

Moreover, in this embodiment, the also includes one or more electriccircuits and/or software modules relating to measurement analysismodels, adaptive interval calculation and the like.

In contrast to end-to-end based network tomography approaches, theembodiments herein uses the data provided by intermediary nodes, such asMIPs, resulting in the following advantages:

A reduction of the complexity in computing a set of measurementscovering an end-to-end topology is achieved.

A reduced number of measurements for detection a condition on a link areneeded. Detection of the condition, such as changes, degradations,modeling and localization, may be done based on already performedmeasurements. Thus, the need for additional diagnostic measurements isreduced. The adaptive delay based on the measurements reduces the linkload induced by probes in comparison to use of fixed probing intervals.

The embodiments are reliable in stationary as well as non-stationaryconditions.

In order to further illustrate examples, advantages and implementationof the embodiments disclosed, the following discussion is provided.

“P. Varga and I. Moldován. Integration of Service-Level Monitoring withFault Management for End-to-End Multi-Provider Ethemet Services. IEEETransactions on Network and Service Management 4(1) (2007) 28-38”describe a fault management framework for service-level monitoring inEthernet services “R. Santitoro. Metro Ethemet Services—A TechnicalOverview. MEF, http://www.metroethemefforum.org.”, based on recommendedperformance metrics defined in MEF 10.1 “Ethemet Services AttributesPhase 2, Metro Ethernet Forum, Technical Specification MEF 10.1,November 2006” and Y.1731 “ITU-T Rec. Y.1731, OAMFunctions andMechanisms for EthernetBasedNetworks, 02/2008”. The framework is splitin modules taking care of connectivity fault management, performancemonitoring, service-level monitoring, and security. The performancemonitoring is based on, among other things, periodic measurements ofdelays and drop.

Generally, trace-route OAM functions could suffice to measure thelatency or drop, under the condition that a reply message is sentdirectly from each MIP back to the originating MEP (as in ETH-Trace, IPTrace-route or MPLS LSP Trace-route). A combined unicast/multicast basedtrace-route (with packet replication at each hop), similar to ETH-Trace,may be preferable as it effectively measures both drop and latency withsmall additional link load. Unicast based trace-route, with incrementalprobes such as in MPLS LSP or IP networks, can also be used, but may beless efficient in capturing small, quick variations in the networkbehavior, which may affect the estimations to some degree.

Alternatively, when it is of greater importance to capture smallfluctuating variations, it may be necessary to implement a separate OAMprobing function that periodically probes the connection with bursts ofloopback (LB) messages (such as ETH-LBM, MPLS LSP LB etc). This providesmore control over how the measurements are performed, in terms oftransmission delays between probes and additional data exchanges ifnecessary (such as timestamps). An example of control protocol for atool that uses bursts of packets, or “trains”, is to be found in RFC6802, Ericsson Two-Way Active Measurement Protocol (TWAMP) Value-AddedOctets. The bursts of loopback messages, mentioned above, are related toa way of operating a tool, which usually sends only one packet. In orderto generate a burst, the tool—usually sending only one packet—would beinvoked multiple times, where each invocation of the tool follows aprevious invocation directly, or immediately, without delay. Other toolsinclude bursts natively, as specified in RFC6802.

In the previous work presented by Steinert and Gillblad “Long-termadaptation and distributed detection of local network changes. IEEEGLOBECOM 2010, Miami, Fla., USA. 2010” overlapping estimators wereapplied directly on latency measurements performed on one-hopconnections between neighboring nodes. Here, each segment would insteadbe modeled with overlapping estimators such that probes can be sent witha variable delay based directly on expected link delay or on e.g.estimated Gamma parameters, as described in “Long term adaption anddistributed detection of local network changes” as above and “A. G.Prieto, D. Gillblad, R. Steinert, A. Miron. TowardDecentralizedProbabilistic Management. IEEE Communications Magazine. IEEECommunications Magazine, July 2011, volume 49, issue 7, pages 80-86”. Byallowing the probe interval to be set autonomously based on estimatedparameters local network variations can be taken into account in a waythat cannot be done with the use of fixed intervals.

In “R. Steinert and D. Gillblad. Link delay modeling and directlocalization of performance degradations in transport networks. InSubmitted to INFOCOM 2013. IEEE, 2012”, statistical modeling ofintermediate link delay and direct localization of link performancedegradations were addressed, based on deriving link delay estimates fromincremental end-to-end measurements. Two types of models were developed,targeting statistically stationary and non-stationary measurementconditions, combined with adaptive mechanisms that enable directlocalization of performance changes.

The work by Steinert and Gillblad from 2010 and 2011, referred to in thepreceding paragraph, applies to one link segment, generally betweennodes that are directly connected. Depending on the network conditions,it would also apply to estimating the delay on the overall MEP-MEPconnection and the ingress MEP—first MIP connection. However, theseresults cannot be used for estimating delays or loss for the segmentsbetween the MIPs, unless each MIP is made able to initiate activemeasurements. This would add a considerable management overhead andpotentially cost to the nodes. The work submitted to INFOCOM 2013. IEEEin 2012, as mentioned above, extends the delay estimates to end-to-endpaths, but does not address loss modeling and does not address theautomation aspects of choosing the model adapted to the networkconditions and removing the need to configure thresholds by using changedetection.

As an example, probing intervals can be adjusted using a variable delayτ based on the aggregated expected latency obtained from the per segmentX_(i) estimated Gamma parameters Θ_(i) of the measured latencies (n isthe number of segments):

$\begin{matrix}{\tau = {{\sum\limits_{i = 1}^{n}{\tau \left( \Theta_{i} \right)}} = {\sum\limits_{i = 1}^{n}{c_{i}{f_{i}^{- 1}\left( p_{i} \right)}}}}} & {{equation}\mspace{14mu} 5}\end{matrix}$

The probing delay is controlled with a cost c_(i) and a fraction p_(i)of the inverted cumulative density function ƒ_(i) ⁻¹(p), based on theGamma distribution P(t) of observed delays Δt:

ƒ(Δt)=∫₀ ^(Δt) P(t)dt  equation 6

The parameter c can be regarded as controlling the trade-off between theamount of induced link load and reaction time to observed delays, e.g.for decision-making of configuration changes. The fraction p representsthe amount of probe responses that has been observed within a certaindelay. See R. Steinert, D. Giliblad. Long-term adaptation anddistributed detection of local network changes. IEEE GLOBECOM 2010,Miami, Fla., USA. 2010.

As used herein, the terms “number”, “value” may be any kind of digit,such as binary, real, imaginary or rational number or the like.Moreover, “number”, “value” may be one or more characters, such as aletter or a string of letters. “number”, “value” may also be representedby a bit string.

Even though embodiments of the various aspects have been described, manydifferent alterations, modifications and the like thereof will becomeapparent for those skilled in the art. The described embodiments aretherefore not intended to limit the scope of the present disclosure.

1. A method for evaluating a link between a first network node and asecond network node, wherein the link is configured to carry datapackets between the first and second network nodes via at least onethird network node, wherein the link comprises at least a first segmentconfigured to carry data packets between the first and third networknodes and a second segment configured to carry data packets between thesecond and third network nodes, the method comprising: obtaining anindication of a measurement tool to be used in a measurement session forevaluation of the link; selecting a mathematical model based on theindication of the measurement tool; generating a set of measurementvalues by executing the measurement session while using the measurementtool according to the indication of the measurement tool; determining afirst and a second value relating to lost data packets of the first andsecond segments, respectively, based on the set of measurement valuesand the selected mathematical model; and identifying at least one of thefirst and second segments based on the first and second values.
 2. Themethod according to claim 1, wherein the identifying of at least one ofthe first and second segments comprises selecting a predefined number ofthe first and second segments for which the respective first and secondvalues are the greatest among the first and second values.
 3. The methodaccording to claim 1, wherein the identifying of at least one of thefirst and second segments comprises selecting one of more of the firstand second segments for which the respective first and second values aregreater than a first predetermined threshold value for lost datapackets.
 4. The method according to claim 1, wherein the identifying ofat least one of the first and second segments comprises determining arespective value indicative of a change in terms of lost data packetsfor each of the first and second segments based on the first and secondvalue, respectively; and selecting one or more of the first and secondsegments for which the respective value indicative of the change isgreater than a second predetermined threshold value for changedetection.
 5. The method according to claim 1, further comprising:identifying a set of mathematical models adapted to the measurementsession while taking into account whether data packets carried on thelink are modeled under a statistical condition of stationarity ornon-stationarity, wherein the selected mathematical model is selectedfrom the set of mathematical models.
 6. The method according to claim 1,wherein the link is a multi-segment Ethernet link, multi-segmentInternet Protocol link, a multi-segment pseudo-wire or a MPLS-TP labelswitched path.
 7. The method according to claim 1, wherein the selectedmathematical model is equation 1 or 2, wherein equation 1 is${E\left( X_{i} \right)} = \frac{E\left( Y_{i} \right)}{E\left( Y_{i - 1} \right)}$and equation 2 is${{E\left( X_{i} \right)} = \frac{{E\left( {Y_{i}Y_{i - 1}} \right)} - {{Cov}\left( {Y_{i},Y_{i - 1}} \right)}}{{E\left( Y_{i - 1} \right)}^{2}}},$where Xi is a segment, E(Xi) is the estimated success rate on thesegment Xi, E(Yi) is the observed success rate for link Yi, Cov iscovariance.
 8. The method according to claim 1, being performed by anetwork entity (140).
 9. The method according to claim 8, wherein thenetwork entity is the first network node.
 10. The method according toclaim 8, wherein the network entity is a network management node. 11.The method according to claim 9, wherein the obtaining of the indicationof the measurement tool comprises receiving the indication of themeasurement tool from a network management node.
 12. The methodaccording to claim 10, wherein the obtaining of the indication of themeasurement tool comprises receiving the indication of the measurementtool from an operator.
 13. A network entity configured to evaluate alink between a first network node and a second network node, wherein thelink is configured to carry data packets between the first and secondnetwork nodes (110, 120) via at least one third network node, whereinthe link comprises at least a first segment configured to carry datapackets between the first and third network nodes (110, 130) and asecond segment configured to carry data packets between the second andthird network nodes (120, 130), the network entity comprising: a memory;a processing circuit configured to execute instructions contained in thememory which, when executed by the processing circuit, are configured tocause the network entity to: obtain an indication of a measurement toolto be used in a measurement session for evaluation of the link; select amathematical model based on the indication of the measurement tool;generate a set of measurement values by executing the measurementsession while using the measurement tool according to the indication ofthe measurement tool; determine a first and a second value relating tolost data packets of the first and second segments, respectively, basedon the set of measurement values and the selected mathematical model;and identify at least one of the first and second segments based on thefirst and second values.
 14. The network entity according to claim 13,wherein the instructions, when executed by the processing circuit, arefurther configured to cause the network entity to select a predefinednumber of the first and second segments for which the respective firstand second values are the greatest among the first and second values.15. The network entity according to claim 13, wherein the instructions,when executed by the processing circuit, are further configured to causethe network entity to select one of more of the first and secondsegments for which the respective first and second values are greaterthan a first predetermined threshold value for lost data packets. 16.The network entity according to claim 13, wherein the instructions, whenexecuted by the processing circuit, are further configured to cause thenetwork entity to: determine a respective value indicative of a changein terms of lost data packets for each of the first and second segmentsbased on the first and second value, respectively; and select one ormore of the first and second segments for which the respective valueindicative of the change is greater than a second predeterminedthreshold value for change detection.
 17. The network entity accordingto claim 13, wherein the instructions, when executed by the processingcircuit, are further configured to cause the network entity to: identifya set of mathematical models adapted to the measurement session whiletaking into account whether data packets carried on the link are modeledunder a statistical condition of stationarity or non-stationarity,wherein the selected mathematical model is selected from the set ofmathematical models.
 18. The network entity according to claim 13,wherein the link is a multi-segment Ethernet link, multi-segmentInternet Protocol link, a multi-segment pseudo-wire or a MPLS-TP labelswitched path.
 19. The network entity according to claim 13, wherein theselected mathematical model is equation 1 or 2, wherein equation 1 is${{E\left( X_{i} \right)} = \frac{E\left( Y_{i} \right)}{E\left( Y_{i - 1} \right)}},$and equation 2 is${{E\left( X_{i} \right)} = \frac{{E\left( {Y_{i}Y_{i - 1}} \right)} - {{Cov}\left( {Y_{i},Y_{i - 1}} \right)}}{{E\left( Y_{i - 1} \right)}^{2}}},$where Xi is a segment, E(Xi) is the estimated success rate on thesegment Xi, E(Yi) is the observed success rate for link Yi, and Cov iscovariance.
 20. The network entity according to claim 13, wherein thenetwork entity is the first network node.
 21. The network entityaccording to claim 13, wherein the network entity is a networkmanagement node.
 22. The network entity according to claim 20, whereinthe instructions, when executed by the processing circuit, are furtherconfigured to cause the network entity to receive the indication of themeasurement tool from a network management node.
 23. The network entityaccording to claim 21, wherein the instructions, when executed by theprocessing circuit, are further configured to cause the network entityto receive the indication of the measurement tool from an operator.